Abstract

In this paper, we conduct a detailed study of theYouTube CDN with a view to understanding the mechanismsand policies used to determine which data centers users downloadvideo from. Our analysis is conducted using week-long datasetssimultaneously collected from the edge of five networks - twouniversity campuses and three ISP networks - located in threedifferent countries. We employ state-of-the-art delay-based geolo-cation techniques to find the geographical location of YouTubeservers. A unique aspect of our work is that we perform ouranalysis on groups of related YouTube flows. This enables us toinfer key aspects of the system design that would be difficultto glean by considering individual flows in isolation. Our resultsreveal that while the RTT between users and data centers plays arole in the video server selection process, a variety of other factorsmay influence this selection including load-balancing, diurnaleffects, variations across DNS servers within a network, limitedavailability of rarely accessed video, and the need to alleviatehot-spots that may arise due to popular video content.